Search NASA⌕ Search

NASA NTRS · 20240011787

An Evaluation of Composite Primary Structure for Habitat Modules

Abstract

Boeing studied the use of composite structures in deep space habitats. Compared to metallic modules, composite structures offer increased strength and stiffness with reduced mass. The study evaluated the design considerations and the mass benefits of an all composite primary structure. The composite module design included a continuous pressure shell, composite longerons, and circumferential ring frames. The composite material selected was a high strength carbon fiber and epoxy slit tape prepreg. The manufacturing involved automated fiber placement and utilizing existing facilities and autoclaves. Analysis showed a 33% reduction in primary structure mass and an overall net reduction of nearly 1900 kg compared to metallic modules. The composite module demonstrated improved mass efficiency and opens new possibilities for mission performance in deep space exploration.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Matthew T Ziglar, Tin A Luu. An Evaluation of Composite Primary Structure for Habitat Modules. https://ntrs.nasa.gov/citations/20240011787

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

One-pot synthesis of mechanically robust hierarchical graphene-polyethylene aerogels

Fabricating mechanically robust graphene aerogels (GAs) without compromising their notable features including superelasticity, large surface area, high porosity, and low density is challenging. Here, this work presents a new one-pot strategy based on ambient drying to fabricate a three-dimensional (3D) graphene-polyethylene aerogel (G-PEA) with a unique hierarchical porous structure, in which the highly porous polyethylene is encapsulated by graphene frameworks. The hierarchical G-PEA exhibited substantially enhanced compressive strength while maintaining low density and superelasticity comparable to those of bare GAs. The G-PEAs with 5 wt% PE (G-PEA5) showed a significant improvement (up to 2083 %) in compressive stress compared to bare GAs, which can be attributed to the porous PE support within the GA framework. The G-PEA5 retained 94% of its compressive stress after 100 compression cycles, which is still higher than that (∼80%) of bare GAs, and maintained good elastic recovery. The designed hierarchical G-PEAs show great promise in the applications that require outstanding mechanical properties.

Composite↗

Prediction of Stiffness and Fatigue Lives of Polymer Matrix Composite Laminates Using Artificial Neural Networks

Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, both Python and MATLAB-based neural nets have been developed to predict initial stiffness and fatigue life of an eight-ply symmetric polymer matrix composite laminate. Two types of neural networks, a Multilayer Perceptron (MLP) and a Recurrent Neural Network (RNN), have been developed for both platforms. Results show that the both neural net types can provide an excellent estimate of initial stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminate. RNNs are better able to capture the shape of the fatigue curve of a laminate. This tool can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. The associated surrogate models could also be used in composite multiscale analyses to replace the actual physics-based calculations at lower scales and thereby significantly increase the computational efficiency of such analyses and thus make multiscale analyses a viable industrial tool for large scale structural problems.

Composite↗

Simulation of Guided-Wave Ultrasound Propagation in Composite Laminates: Benchmark Comparisons of Numerical Codes and Experiment

Ultrasonic wave methods constitute the leading physical mechanism for nondestructive evaluation (NDE) and structural health monitoring (SHM) of solid composite materials, such as carbon fiber reinforced polymer (CFRP) laminates. Computational models of ultrasonic wave excitation, propagation, and scattering in CFRP composites can be extremely valuable in designing practicable NDE and SHM hardware, software, and methodologies that accomplish the desired accuracy, reliability, efficiency, and coverage. The development and application of ultrasonic simulation approaches for composite materials is an active area of research in the field of NDE. This paper presents comparisons of guided wave simulations for CFRP composites implemented using four different simulation codes: the commercial finite element modeling (FEM) packages ABAQUS, ANSYS, and COMSOL, and a custom code executing the Elastodynamic Finite Integration Technique (EFIT). Benchmark comparisons are made between the simulation tools and both experimental laser Doppler vibrometry data and theoretical dispersion curves. A pristine and a delamination type case (Teflon insert in the experimental specimen) is studied. A summary is given of the accuracy of simulation results and the respective computational performance of the four different simulation tools.

Composite↗